In [1]:
import pandas as pd
import numpy as np 
import matplotlib.pyplot as plt
# import seaborn as sns 
import datetime
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
from plotly.graph_objs import Scatter, Figure, Layout
import plotly
import plotly.graph_objs as go
import plotly.express as px
from IPython.display import Markdown as md
init_notebook_mode(connected=False)
import io
import requests
import re

COVID-19 in Italy. Visuals

(alternatively, see results and code together here)

 

Data source: this GitHubi page

Authors and sources mentioned: Editore/Autore del dataset: Dipartimento della Protezione Civile. Categoria ISO 19115: Salute. Dati forniti dal Ministero della Salute.

Regional data files (Dati per Regione):
  • Struttura file giornaliero: dpc-covid19-ita-regioni-yyyymmdd.csv (dpc-covid19-ita-regioni-20200224.csv)
  • File complessivo: dpc-covid19-ita-regioni.csv
  • File ultimi dati (latest): dpc-covid19-ita-regioni-latest.csv

 

In [2]:
URL='https://it.wikipedia.org/wiki/Regione_(Italia)'
res=requests.get(URL)
tables=pd.read_html(res.text)
dt = tables[13]
In [3]:
def dewhite(x):
    ''.join(re.findall('\d+', x))

dt2 = dt[['Regione','Popolazione (ab.)']].copy()
dt2.columns = ['Region','Pop']
    
dt2.Pop = dt2.Pop.apply(lambda x: ''.join(re.findall('\d+', x))).astype(int)
In [4]:
s = requests.get("https://raw.githubusercontent.com/pcm-dpc/COVID-19/master/dati-regioni/dpc-covid19-ita-regioni.csv").content
dat = pd.read_csv(io.StringIO(s.decode('utf-8')))
cdate = dat.data.max()

md("Currently data as of date: {}".format(cdate))
Out[4]:

Currently data as of date: 2021-01-23T17:00:00

 

What's in the original dataframe?

In [5]:
md("All column names: {}".format(dat.columns.tolist()))
Out[5]:

All column names: ['data', 'stato', 'codice_regione', 'denominazione_regione', 'lat', 'long', 'ricoverati_con_sintomi', 'terapia_intensiva', 'totale_ospedalizzati', 'isolamento_domiciliare', 'totale_positivi', 'variazione_totale_positivi', 'nuovi_positivi', 'dimessi_guariti', 'deceduti', 'casi_da_sospetto_diagnostico', 'casi_da_screening', 'totale_casi', 'tamponi', 'casi_testati', 'note', 'ingressi_terapia_intensiva', 'note_test', 'note_casi', 'totale_positivi_test_molecolare', 'totale_positivi_test_antigenico_rapido', 'tamponi_test_molecolare', 'tamponi_test_antigenico_rapido', 'codice_nuts_1', 'codice_nuts_2']

In [7]:
df = dat.drop(['stato','codice_regione'], axis=1)
df.columns = ['Date','Region','Lat','Long','HospWithSymptoms','IC','HospTotal','AtHome','CurrentlyPositive','VariationOfPositives','NewPositives','Recovered', 'Deaths','Diagnostico','Screening','TotalCases','NoOfTests','casi_testati','note','ingr_ter_intens','note_test','note_casi', 'totale_positivi_test_molecolare', 'totale_positivi_test_antigenico_rapido', 'tamponi_test_molecolare', 'tamponi_test_antigenico_rapido', 'codice_nuts_1', 'codice_nuts_2']

df = pd.merge(df, dt2, left_on='Region', right_on='Region')

df['Date'] = pd.to_datetime(df['Date']).dt.date
df = df.set_index(df["Date"])
df.index = pd.to_datetime(df.index)

df['NewPositives'] = np.abs(df['NewPositives'])

dat.tail(5)
Out[7]:
data stato codice_regione denominazione_regione lat long ricoverati_con_sintomi terapia_intensiva totale_ospedalizzati isolamento_domiciliare ... note ingressi_terapia_intensiva note_test note_casi totale_positivi_test_molecolare totale_positivi_test_antigenico_rapido tamponi_test_molecolare tamponi_test_antigenico_rapido codice_nuts_1 codice_nuts_2
7030 2021-01-23T17:00:00 ITA 19 Sicilia 38.115697 13.362357 1444 223 1667 45960 ... NaN 14.0 NaN NaN 128877.0 0.0 1431974.0 151914.0 ITG ITG2
7031 2021-01-23T17:00:00 ITA 9 Toscana 43.769231 11.255889 629 107 736 7525 ... NaN 8.0 NaN NaN 130295.0 38.0 2078051.0 45882.0 ITG ITG1
7032 2021-01-23T17:00:00 ITA 10 Umbria 43.106758 12.388247 292 46 338 4691 ... NaN 5.0 NaN NaN 33822.0 0.0 567260.0 11238.0 ITI ITI2
7033 2021-01-23T17:00:00 ITA 2 Valle d'Aosta 45.737503 7.320149 43 2 45 282 ... NaN 0.0 NaN NaN 7720.0 0.0 67704.0 444.0 ITC ITC2
7034 2021-01-23T17:00:00 ITA 5 Veneto 45.434905 12.338452 1843 286 2129 45412 ... Nei valori riportati per le terapie intensive ... 18.0 NaN NaN 304717.0 0.0 3657997.0 222352.0 ITH ITH3

5 rows × 30 columns

 

Variable names to English and their explanation

  • HospWithSymptoms : Currently hospitalized patients with symptoms
  • IC : Intensive care
  • HospTotal: Total number of currently hospitalized patients
  • AtHome : Currently at home confinement
  • CurrentlyPositive : Total amount of current positive cases (Hospitalised patients + Home confinement)
  • NewPositives : New amount of positive cases (Actual total amount of current positive cases - total amount of current positive cases of the previous day)
  • TotalCases : Total amount of positive cases
  • NoOfTests : Tests performed
In [8]:
df.tail()
Out[8]:
Date Region Lat Long HospWithSymptoms IC HospTotal AtHome CurrentlyPositive VariationOfPositives ... ingr_ter_intens note_test note_casi totale_positivi_test_molecolare totale_positivi_test_antigenico_rapido tamponi_test_molecolare tamponi_test_antigenico_rapido codice_nuts_1 codice_nuts_2 Pop
Date
2021-01-19 2021-01-19 Veneto 45.434905 12.338452 2164 318 2482 60689 63171 -5912 ... 27.0 NaN NaN 300127.0 0.0 3598134.0 108882.0 ITH ITH3 4905854
2021-01-20 2021-01-20 Veneto 45.434905 12.338452 2096 315 2411 55058 57469 -5702 ... 16.0 NaN NaN 301486.0 0.0 3615256.0 136074.0 ITH ITH3 4905854
2021-01-21 2021-01-21 Veneto 45.434905 12.338452 1991 305 2296 51261 53557 -3912 ... 23.0 NaN NaN 302489.0 0.0 3630227.0 157055.0 ITH ITH3 4905854
2021-01-22 2021-01-22 Veneto 45.434905 12.338452 1935 291 2226 47624 49850 -3707 ... 15.0 NaN NaN 303687.0 0.0 3644507.0 186365.0 ITH ITH3 4905854
2021-01-23 2021-01-23 Veneto 45.434905 12.338452 1843 286 2129 45412 47541 -2309 ... 18.0 NaN NaN 304717.0 0.0 3657997.0 222352.0 ITH ITH3 4905854

5 rows × 29 columns

 

daily numbers & moving averages (MA)

(double click and click on legend to select one or multiple regions in the graph)

In [9]:
df2 = df

fig = px.line(df2, x="Date", y="NewPositives", color="Region", hover_name="Region",
        render_mode="svg", log_y=False)
fig.update_layout(title="Daily new positive cases")
fig.show()
In [10]:
df2['MovAv7'] = df2['NewPositives'].rolling(window=7).mean()

fig = px.line(df2[df2.index>'2020-3-1'], x="Date", y="MovAv7", color="Region", hover_name="Region",
        render_mode="svg", log_y=False)
fig.update_layout(title="7-day MA of new positive cases")
fig.show()
In [11]:
df2['NewPos_per_100K'] = df2['NewPositives']/df2['Pop']*1000_00

df2['NewPos_per_100K'] = df2['NewPos_per_100K'].rolling(window=7).mean()

fig = px.line(df2[df2.index>'2020-3-1'], x="Date", y="NewPos_per_100K", color="Region", 
              hover_name="Region", log_y=False)
fig.update_layout(title="7-day MA of new positive cases, per 100K")
fig.show()
In [12]:
df2['PosTests'] = df2['NewPositives']/df2['NoOfTests']*100

fig = px.scatter(df2, y="PosTests", x="Date", color="Region", 
              hover_name="Region", log_y=True)
fig.update_layout(title="Percentage of positive tests")
fig.show()
In [13]:
df2['IC_per_100K'] = df2['IC']/df2['Pop']*1000_00

fig = px.line(df2, x="Date", y="IC_per_100K", color="Region", hover_name="Region",
        render_mode="svg", log_y=False)
fig.update_layout(title="Current number of intensive care patients, per 100K")
fig.show()
In [14]:
df2['Hosp_per_100K'] = df2['HospTotal']/df2['Pop']*1000_00

fig = px.line(df2, x="Date", y="Hosp_per_100K", color="Region", hover_name="Region",
        render_mode="svg", log_y=False)
fig.update_layout(title="Current number of hospitalized, per 100K")
fig.show()
In [15]:
df3 = df2.copy()

df3['NewDeaths'] = df3['Deaths'] - df3.groupby(['Region'])['Deaths'].transform('shift')

fig = px.bar(df3, x=df3['Date'], y="NewDeaths", color="Region", hover_name="Date")
fig.update_layout(title="Daily number of deaths")
fig.show()
In [16]:
df2['Deaths_per_100K'] = (df2['Deaths']/df2['Pop'])*1000_00
fig = px.line(df2, x="Date", y="Deaths_per_100K", color="Region", 
              hover_name="Region", render_mode="svg", line_shape='spline')
fig.update_layout(title="Cumulative number of deaths, per 100K")
fig.show()
In [17]:
df2['Change_per_100K'] = df2['VariationOfPositives']/df2['Pop']*1000_00
df2['Change_per_100K'] = df2['Change_per_100K'].rolling(window=7).mean()

fig = px.line(df2[(df2.index>'2020-3-1') & (df2['Region']!="""Valle d'Aosta""")], x='Date', y="Change_per_100K", 
              color="Region", hover_name="Date")
fig.update_layout(title="7-day MA of change in current positive cases, per 100K (excl. Valle d'Aosta)")
fig.show()
In [18]:
df2['Current_per_100K'] = df2['CurrentlyPositive']/df2['Pop']*1000_00
df2['Current_per_100K'] = df2['Current_per_100K'].rolling(window=14).mean()

fig = px.line(df2[(df2.index>'2020-3-7')], x='Date', y="Current_per_100K", color="Region", hover_name="Date")
fig.update_layout(title="14-day MA of current positive cases, per 100K")
fig.show()

 

All regions together

In [19]:
df2 = df
df_sum = df2.drop(['Lat','Long'], axis=1).groupby(df.Date).sum().reset_index()

df_sum2 = pd.melt(df_sum, id_vars=['Date'], value_vars=['NewPositives','IC','HospTotal','CurrentlyPositive'])

fig = px.line(df_sum2, x="Date", y="value", color='variable', hover_name="value", render_mode="svg", log_y=True, 
              line_shape='spline')
fig.update_layout(title="Number of new and current positives, current IC patients and currently hospitalized")
fig.show()